The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
With the advent of affordable and relatively fast whole genome sequencing, significant quantities of detailed knowledge on the DNA level have become available. However, meaningful analysis of the data has been impeded in most cases by the sheer volume of information and lack of infrastructure and computing algorithms. Such difficulties are further compounded where additional omics information is available for analysis, and especially RNomics and proteomics on a tissue and even cellular level. Thus, integration of such additional data has become a rate-limiting step in many prognostic, diagnostic, and therapeutic approaches.
More recently, and as for example described in US 2012/0059670 and US 2012/0066001, high throughput sequence analysis for genomics data has become substantially more efficient by incremental differential alignment and comparison of a patient's tumor and matched healthy tissue. Such information can then be further analyzed using a pathway recognition algorithm as also previously described in WO/2011/139345 and WO/2013/062505. However, even with these advanced tools, presence of a particular constellation of mutations in a tumor genome does not necessarily predict that the mutated gene is actually expressed, and if so, what effect the mutation might have. While findings per se from RNomics may be helpful, such results in isolation will typically not be of high informative value without contextual additional data from genomics and proteomics.
Thus, even though numerous systems and methods for analyzing omics data are known in the art, there is still a need to improve omics analysis and integration of information gleaned from various omics platforms.